Convolutional layers are usually stacked and interleaved with other types of layers, such as nonlinearities, down(up)sampling, skip connections, normalization, regularization, etc.
In this study, we use BatchNorm [1]}, ReLU [2]}, Dropout [3]}, and no skip connections.
In order to promote long-distance integration, we propose to apply convolutions not only in the \((x,y)\)  plane, but also in the \((x,z)\)  and \((y,z)\)  planes.
With some abuse of notation, we denote \(I\)  as the output of the previous layer, and \(S\)  as the output of the current layer. Then the next convolution is given by
\(S(x,y,z) = \sum _{dx,y^{\prime },dz} K(dx, dz,y,y^{\prime }) I(x+dx,y^{\prime },z+dz).\) 
